UPTEC F11047

Examensarbete 30 hpOctober 2011

Database and Modeling of Field Test Data from Lithium Ion Batteries in Hybrid Electrical Vehicles

Niclas Höök

Teknisk- naturvetenskaplig fakultet UTH-enheten Besöksadress: Ångströmlaboratoriet Lägerhyddsvägen 1 Hus 4, Plan 0 Postadress: Box 536 751 21 Uppsala Telefon: 018 – 471 30 03 Telefax: 018 – 471 30 00 Hemsida: http://www.teknat.uu.se/student

Abstract

Database and Modeling of Field Test Data fromLithium Ion Batteries in Hybrid Electrical Vehicles

Niclas Höök

In this thesis information received from a hybrid vehicle battery test equipment wasstructured and analyzed. This test equipment is currently placed on a fleet of Scaniatrucks with the purpose of emulating hybrid vehicle environment on battery cell level.A Microsoft Access database structure was set up in order to make it possible to savetest data in a structured way. In addition, Matlab scripts were made with the purposeof calculating cell aging from pulse- and capacity tests. Furthermore, drive cycleanalysis was performed looking at statistics for selected parameters. Data collectedfrom late October 2010 until beginning of July does not yet show any aging of the fieldtested battery cells regarding capacity loss or resistance increase. The internalresistance of the batteries was calculated to 2 to 4 milli ohm and the capacity wasfrom the tests found to be around 3 ampere hours. The energy efficiency, which wascalculated from pulse test data, shows an efficiency between 95 to 97%.

ISSN: 1401-5757, UPTEC F11 047Examinator: Tomas NybergÄmnesgranskare: Torbjörn GustafssonHandledare: Pontus Svens

Contents1 Introduction 2

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Aim of thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Theory 32.1 Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.1.1 Relational database model . . . . . . . . . . . . . . . . . . 32.1.2 Query Language . . . . . . . . . . . . . . . . . . . . . . . 52.1.3 Other database models . . . . . . . . . . . . . . . . . . . . 5

2.2 Test equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.3 Batteries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.3.1 Aging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.3.2 C-rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.3.3 State of Charge . . . . . . . . . . . . . . . . . . . . . . . . 102.3.4 Capacity test . . . . . . . . . . . . . . . . . . . . . . . . . 122.3.5 Internal resistance and Pulse test . . . . . . . . . . . . . . 13

3 Method 163.1 Database development . . . . . . . . . . . . . . . . . . . . . . . . 163.2 Matlab script development . . . . . . . . . . . . . . . . . . . . . . 18

4 Results 204.1 Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204.2 Matlab scripts . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.2.1 Capacity test . . . . . . . . . . . . . . . . . . . . . . . . . 224.2.2 Pulse test . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.3 Drive cycle analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 264.3.1 Temperature . . . . . . . . . . . . . . . . . . . . . . . . . 264.3.2 Vehiclespeed . . . . . . . . . . . . . . . . . . . . . . . . . 284.3.3 SOC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

5 Discussion and Conclusions 32

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1 Introduction

1.1 BackgroundEmissions from vehicles have been significantly reduced over the past decades.It is becoming more and more di!cult finding ways reducing the emissions,but one way of doing so is through hybridization using lithium-ion batteries.The main problem using batteries as energy storage in hybrid electrical vehiclesis the limited battery lifetime. One way of testing lifetime is to perform fieldtest studies on battery packs. This is a costly process since it requires bothhybridized vehicles as well as full scale battery packs. In addition, many di"erenttypes of batteries are available today and therefore it becomes a di!cult processfinding an easy way comparing them.

A way to address these problems is the usage of a test equipment placed onvehicles, in this project Scania trucks, which makes a battery cell experiencethe same environmental and cycling conditions as a battery pack in a hybridelectrical vehicle. With this kind of test equipment it is possible to measurebattery performance periodically during a complete lifetime test. By placingthe test equipment on multiple vehicles a wider range of information about thecells can be obtained and the quality of the results improved. Since both batteryparameters such as state of charge, current and vehicle data such as velocityetcetera are stored in the test equipment used in this work, drive cycle analysiscan be made, making it possible measuring battery performance for di"erentdrive patterns over time.

1.2 Aim of thesisThe aim of the thesis is to extract information about battery performance andaging from field test data collected from trucks. A database was created inMicrosoft Access in order to be able to save the field test data. In addition, tobe able to determine the aging of the battery cells scripts were made in Matlabwhere calculations based on pulse- and capacity tests were made. Furhermore,drive cycle analysis were performed looking at statistics for selected parameters.Data from two di"erent trucks with same hybrid strategies but di"erent drivepatterns were collected and analyzed.

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2 Theory

2.1 DatabaseA database is an environment where data is stored. The data which is stored isoften in some way related and could for example describe a car such as model,color and registration number. A database can di"er a lot in size dependingon purpose and area of usage and also vary a lot in complexity. A databasemanagement system (DBMS) is a system containing programs that give usersan opportunity creating and maintaining a database. It defines, constructs,manipulates and shares the database.

In order to define a database, the user has to specify data types, structuresand constraints about the data that is going to be stored in the database. Con-structing the database is the process of storing data on some storage medium.Manipulating the database means the usage of functions such as querying thedatabase to retrieve data, updating in order to see changes and generating re-ports from the data. Sharing the database simply means that multiple usersand programs are allowed to access the database. In addition, it is important toprotect the database against hardware or software malfunction and also main-taining the database as requirements change over time.[1].

The first database systems came out during the mid-1960s.Due to the earlystage in the development of computers in general and the requirements fromthe users they were implemented on large and expensive mainframe computers.The main types of early systems were hierarchical systems and network modelbased systems. What characterises hierarchical systems is that it is structuringits data as a tree of records where each record has one parent record and manychildren whereas the network model allows each record to have multiple parentand children records.[2, 3].

The relational model was introduced in 1970 by the computer scientist TedCodd and has become one of the most popular model used in commercialdatabases. The model is based on mathematical relations and the reason ofits popularity is due to its simplicity and mathetmatical foundation. Populardatabases today are Oracle, SQL server and Microsoft Access.

2.1.1 Relational database model

The relational model is a collection of relations. Each relation can be seenas a table of values. Each row in the table is called a tuple and representsinformation about an object, for instance a student’s name,age etcetera. Everyfield or column is called an attribute and helps interpreting the values in eachtuple. Each column contains always the same data type and is representedby a domain of possible values. An example of domain could be an attribute’Gender’ containing either ’M’ for male or ’F’ for female. A column can onlybe connected to one domain whereas one domain can be connected to severalcolumns.

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Figure 1: Database table also known as relation describing the di"erent parts ofa database table. Each column is called attribute and each row is called tuple.

There are several restrictions or constraints that are important in the rela-tional model. Domain constraints means that within each row every attributemust have an atomic value specified by a domain. An atomic value means thatthere can only be one value in the intersection between a tuple and an attribute.There can neither be any duplicates of tuples. Every relation must have tuplesthat are distinct, which means that two tuples cant have the same set of at-tributes. If an attribute contain values that are unique for each tuple, thosecan be used as keys. These are called key constraints. If a tuple contains morethan one unique attribute the values are called candidate keys. For examplea car can have both serial number and engine number that are unique to thecar. One of these candidate keys can, arbitrary, be chosen as a primary key,which means that the primary key uniquely identifies the tuple. Entity integrityconstraint means that no primary key can contain a null value. If that wouldbe the case it would lead to the inability of identifying that tuple. Key andentity integrity constraints are subject to individual tables (relations). Whenthere are more than one table it is important that a tuple in one table that isreferring to another table is also referring to a tuple within that table. This iscalled referential integrity constraint.

A relational model has a two types of operations; retrievals and updates.Retrieval operations are used to query the database. Every query can be seen asa new relation (table) where the result is stored. Example of retrieval operationsare select, project and rename. The update operation is used to modify the datain the table. Example of update operations are insert, delete and update. Ifhaving multiple tables it is preferable using di"erent join operations such as equijoin, inner join and natural join. In addition there are also aggregate functions,which are used to perform calculations on collections of values in the database.Example of aggregate functions are sum, average, maximum, minimum andcount, where count is used to calculate tuples.

When designing the database, it is important to define the relations in orderto avoid redundant information. Data redundancy occurs when the same fieldsare present in multiple tables. This could in turn lead to update anomalies suchas insertion-, deletion- and modification anomalies. To avoid redundancy andanomalies it is important to normalize the data. The most important forms of

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normalizing data are first-, second-, third- and Boyce-Codd normal form. Themain di"erence between the forms are that the higher order of normalized datathe less chance there is for redundant information and anomalies.[1]

2.1.2 Query Language

A relational database uses a query language when it is desirable to manage thedata within a table or multiple tables. The most commonly used query languagein commercial relational databases is SQL, which stands for Structured QueryLanguage. A joint e"ort by ANSI (the American National Standards Institute)and ISO (the International Standards Organisation) has led to a standardisationof SQL. The first standardised version of SQL was SQL-86, later on came alsoa revised version called SQL-92 and finally the latest version SQL-99. Dueto the standardisation of the SQL language it has become less problematicconverting to other database management systems, for example from network-or hierachical systems to relational systems. For as long as the user followsthe standards, within the programming language, switching systems should bemuch simplified, not too expensive and less time consuming.[1]

SQL is both a DDL (data definition language) and DML (data manipulationlanguage), which means that it has statements for both data definition, queryand update. The main command that is used in SQl is the CREATE statement,which can be used to create tables, domains and constructs such as views, as-sertions and triggers. Other common commands used on existing relations areSELECT, FROM, WHERE, GROUP BY, ORDER BY and HAVING.[1]. Anexample of an SQL statement on a table could look like:

SELECT Name, Address, AgeFROM TableWHERE Name=’Anders’ AND Age > 18GROUP BY Age;

The above sql-statement selects the fields Name, Address and Age from thetable Table but only where each row equals the string name ’Anders’ and theinteger where age is above 18. In addition the statement also groups the resultby Age, either in ascending or descending order.

2.1.3 Other database models

Even though relational database systems have become popular in many tradi-tional business applications, there are however situations where there is a needfor more complex database applications. Databases adapted more towards engi-neering design, manufacturing (CAD/CAM), scientific experiments, telecommu-nications, geographic information systems and multimedia have more complexstructures for objects, longer duration transactions, new data types for storingimages etcetera compared to traditional relational models. A database systemthat handles these kind of requirements is an object orientented database. Oneadvantage with this kind of database is that it is better adapted to object ori-ented programming languages such as C++, Smalltalk or Java. This has alsoto some degree been adapted to relational models and therefore there is a ob-ject relational database system. Since the relational system is the database

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used in this theses there wont be any further details about the object orienteddatabases.[1]

2.2 Test equipmentThe test equipment from where the data used in this work came was recentlydeveloped and is used on Scania trucks to obtain data regarding hybrid bat-tery cell aging in field applications.[4]. This test equipment emulates a hybridvehicle environment for the tested battery cell when placed on a conventionaltruck. As can be seen in Figure 2, the test equipment consists of two parts; thebattery management unit (BMU) and the electrical control unit (ECU). TheECU handles the communication with the vehicle and the BMU handles all thebattery cell interactions. The engine management system (EMS), the gearboxmanagement system (GMS) and the brake management system (BMS) simu-lates all the hybrid signals that is missing in a conventional truck. The MGUcalculates the battery cell current based on the information that is being sentfrom the hybrid strategy software and the battery condition (BCON) system.The signals sent from the MGU is channeled through the Controller and intothe BMU when in normal operating mode but not when being in test mode.When in normal operation the BMU continuously sends battery informationto the BCON system within the ECU. In addition, selected vehicle data fromthe truck is directly sent, via the ECU, to the BMU where it is stored. Thesaved data is stored in binary format in the BMU and has to be transferred toa computer and converted to another format in order to be analyzed.

ECU

TEST EQUIPMENT TRUCK

BMS Controller

Interface software

BCON Hybrid strategy software

BMU

EMS GMS

MGU

Figure 2: Test equipment placed on the trucks showing how the ECU commu-nicates with the vehicle and how the BMU that handles all the battery cellinteractions connects to the ECU.[4]

2.3 BatteriesThe battery was invented in the 19th century by the Italian physicist Alessan-dro Volta (1745-1827). A battery contains, simply put, of two electrodes, forexample metal plates (anode and cathode) and some kind of liquid (electrolyte)in between. The anode (negative electrode) is where oxidation takes place and

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cathode (positive electrode) is where reduction takes place during discharge ofa battery. Depending on if the battery is charged or discharged oxidation andreduction will occur on either the negative or the positive electrode but the an-ode and cathode stays the same by convention. The electrochemical potentialon each electrode is determined by the thermodynamic energies related to thechemical reactions at each electrode, where each metal is in equilibrium with itscorresponding ion at the interface between electrode and electrolyte solution, atopen circuit condition. The electrochemical potential for a couple of di"erentmetals can be seen in Table 1.

Ions Potential (V)Li|Li+ -3.045Al|Al3+ -1.706Mn|Mn2+ -1.029Fe|Fe2+ -0.409H2|H+ 0Cu|Cu2+ 0.3402Ag|Ag+ 0.7996F2|F! 2.870

Table 1: The electrochemical potential for di"erent Ions vs. the standard hy-drogen electrode (SHE), which can be used as anode- and cathode material inbatteries.[5]

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!

Figure 3: Discharge of a Li-ion battery showing the structure of the anode andcathode and how the electrons move through the external circuit and how theions move through the electrolyte. When charging the process is reversed.[6].

In Figure 3 a schematic set up of a lithium ion battery is seen. A lithium ionbattery typically contains of a graphite material on a copper current collectoras anode. The electrolyte is mostly an organic solvent based electrolyte sincelithium is very reactive to water. In addition, there is a separator placed inbetween the electrodes whose function is to prevent the electrons from movingbetween the electrodes internally. The cathode is usually some kind of lithiummetal oxide such as lithium manganese- or lithium cobalt oxide with an alu-minum current collector. The electrochemical reactions for anode and cathodewith lithium graphite and an arbitrary metal (M) oxide respectively are:

Plus(cathode) : LiMO2 ! Li1!xMO2 + xLi+ + xe! (1)

Minus(anode) : C6 + xLi+ + xe! ! LixC6 (2)

Lithium ions leaves the graphite structure during discharge and this causesan increased concentration of lithium ions in the electrolyte close to the in-terface between the graphite and electrolyte. As the ions are moving throughthe electrolyte the corresponding electrons are moving through the externalcircuit. When running an external current during discharge energy losses areintroduced due to polarization.These losses includes an activation polarizationwhich is correlated to the energy required for the lithium ions to be incorporatedinto or removed from the electrode material, a concentration polarization whichis caused due to concentration gradients from the mass transport and an ohmicpolarization which relates to resistive losses in the electrolyte, electrodes andterminal connections of the cell.

Mass transportation between the electrodes originates from three di"erent pro-cesses. One is through convection and stirring, second is electrical migration

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due to electric potential gradients and the third and most common and impor-tant is di"usion in a concentration gradient. Di"usion is the main type of masstransfer in most battery systems.

Batteries are electrochemical devices which converts chemical energy into elec-trical energy. The maximum electrical energy that can be delivered depends onthe change in the free energy !G which is known as Gibbs energy (equation3). Because of the energy losses caused by the di"erent polarizations describedabove all this energy cannot be transformed into electric energy. The change inthe energy described by equation 3 is known as the electromotive force whichenables the battery delivering a current to an external circuit.

Wmax = !G = !nFEcell (3)Where n is the amount of electrons, F is faradays constant and Ecell is the cellpotential Ecell = Ec !Ea. The equation above is also known as the electromo-tive force.

2.3.1 Aging

When continously cycling (charging/discharging) a battery, aging mechanismswill result in structural changes of the anode and cathode together with un-wanted chemical reactions with the electrolyte. In Figure 4 examples of agingmechanisms of the anode can be seen.

Figure 4: Aging process of an electrode (anode) showing the creation of an SEIlayer and how it changes as cycling increase.[7].

Since the electrolyte usually has a stable electrochemical potential windowthat is smaller than the cell voltage of a lithium ion battery cell, the electrolyte

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reacts with the anode primary, but also in some degree with the cathode, cre-ating a ’solid electrolyte interphase’ (SEI) on the surface. The SEI is createdafter only a few cycles and protects the anode/cathode from further reactionswith the electrolyte by only letting the lithium ions through. This SEI canchange during time depending on usage of the battery and hence cause an in-crease of the resistance. Due to volume changes of the crystal structure whencharging/discharging a lithium ion battery, there is an increase of mechanicaltensions within the electrode material. This could cause SEI dissolution as wellas capacity loss and higher resistance. In addition, if the battery is charged withto high current at a low temperature lithium plating and dendrites could occur.A proper separator reduces the risk for dendrite growth that could cause shortcircuit of the battery.

2.3.2 C-rate

Discharging batteries can be done at di"erent rates. The discharge rate 1C isdefined as the current needed in order for the battery to discharge in one hour.If a battery with a capacity of 3 Ah is discharged with a rate of 1 C it meansthat a current of 3 A is needed. The reason for using the c-rate definition isto be able to compare di"erent types of batteries. When comparing di"erentbatteries it is important that it is done under similar conditions such as sametemperature, discharge rate and that the batteries have reached the same levelof age.

2.3.3 State of Charge

State of charge (SOC) is the level of charge present in the battery. A fullycharged battery has SOC 100% and a fully discharged battery has SOC 0%.Since the battery capacity decreases over time during usage in for example ahybrid vehicle it is important to always be aware of the present battery capacitysince this value (Qmax) is used when calculating SOC. It is important to keepthe state of charge within certain boundaries in a hybrid vehicle to ensure thebattery lifetime and to be able to optimize the usage of the available battery en-ergy, which in turn makes it possible to obtain lowest possible fuel consumption.The definition of SOC is:

SOC = 100!

1! 1Qmax

"I(t)dt

#(4)

where Qmax is the maximum amount of charge that can be stored within thebattery and I(t) is the current that has passed through the battery since timet=0, starting at SOC=100%.

The state of charge can, in general, be determined through measurement ofthe open circuit voltage and through coulomb counting.

The open circuit voltage (OCV) is the potential di"erence between the twoelectrodes when the battery has no external load connected to it. The OCV atdi"erent SOC for a specific type of battery can be measured through a techniquecalled galvanostatic intermittent titration or GITT. By charging the battery to

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100% SOC followed by discharging the battery in small steps until fully dis-charged while letting the battery obtain a stable OCV between each currentstep, an OCV vs. SOC plot can be obtained using the last OCV reading fromeach rest period. In figure 5 a plot from the GITT technique can be seen wherethe flickering of the graph is due to the current pulses.

0 2 4 6 8 10 12 14 16x 104

1.8

1.9

2

2.1

2.2

2.3

2.4

2.5

2.6

2.7

2.8GITT measurement

Time [s]

Volta

ge [V

]

Figure 5: GITT measurement displaying how the OCV is changing over timeas the battery is fully discharged starting at 100% SOC. The flickering is dueto letting the battery obtain a stable OCV between each current pulse.

The SOC level of the battery can be determined using equation 4 by inte-grating the current over time and determining Qmax. Qmax can be obtainedfrom the GITT-measurement (figure 5) by integrating each current pulse overthe corresponding time interval of each pulse and adding them. From this theOCV of the battery can be plotted as a function of SOC, as shown in figure 6.

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0 10 20 30 40 50 60 70 80 90 1001.8

1.9

2

2.1

2.2

2.3

2.4

2.5

2.6

2.7

2.8

SOC (%)

Volta

ge(V

)

Figure 6: The open circuit voltage as a function of SOC for a lithium ti-tanate/manganese spinel battery. This information can be used for determiningthe SOC of the battery by OCV measurement.

2.3.4 Capacity test

When determining battery aging capacity loss is an important parameter. Thebattery capacity can be determined by discharging a fully charged battery witha constant current until fully discharged. By integrating the current flowingthrough the battery the capacity will be obtained. (See figure 7)

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0 0.5 1 1.5 2 2.5 3 3.51.8

2

2.2

2.4

2.6

2.8

3

Capacity (Ah)

Volta

ge (V

)

Figure 7: Capacity test showing an example of three measurements and how acapacity decrease could look like over time (right to left).

2.3.5 Internal resistance and Pulse test

In addition to the capacity test, a pulse test can be done in order to determinethe internal resistance and energy e!ciency of a battery hence determining theaging. The internal resistance can be determined by charging and dischargingthe battery with a current pulse, in our case (±)60A, and the e!ciency was inour case determined using a current step pulse. (See figure 8).

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4 6 8 10 12 14 16

!60

!40

!20

0

20

40

60

Time (min)

Cu

rre

nt

(A)

Figure 8: Discharging and charging current pulses of (±)60A. Samples are cho-sen at four points in time within each pulse in order to determine the resistanceparameters. The pulses are followed by the step pulse from which the e!ciencywas calculated. In addition a current pulse of 20A can be seen which chargesthe battery up to the next SOC level. The sequence shown is repeated for eachSOC level of 30, 50 and 70 %.

The resistance parameters are calculated at four di"erent time steps withinthe boundaries of each current pulse (18 s) typically at t=0.1, 2, 10 and 17.9,and at three di"erent SOC levels (30, 50, 70). The voltage response related tothe current pulse will have a time dependency mostly caused by concentrationpolarization in the battery and hence, it will take some time for the open circuitvoltage to reach a stable level (figure 9). Therefore an OCV-compensation wasmade to achieve more accurate resistance values.

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4 5 6 7 8 9 10 11 12 13

2.05

2.1

2.15

2.2

2.25

2.3

2.35

2.4

2.45

2.5

2.55

Time (min)

Vo

ltag

e (

V)

Figure 9: Voltage response of the current pulse displayed in figure 8. The OCVis linearized between the start and end of each current pulse and the voltageand the internal resistance is calculated from the di"erence between the actualvoltage and the OCV divided by the corresponding current value at each pointin time.

For each time index related to the start of the current pulse (0,1s 2s 10s and17,9s), the corresponding change in voltage was calculated. The measured volt-age at each time index was subtracted from the linearized open circuit voltageand the voltage drop related to the internal resistance at that specific currentcould hence be calculated. The equations that was used to calculate the resis-tances are presented below:

!UOCV,SOC = (UOCV,t2 ! UOCV,t1) " t/18, 0 # t $ 18 (5)UOCV,SOC = UOCV,t1 + !UOCV,SOC

Rit,dsch = (UOCV,SOC ! Ut)/It, 0 # t $ 18 (6)

The e!ciency can be determined by integrating the power as follows:

dW ="

Pdt ="

U • Idt (7)

!= 100 •Wout/Win (8)

where Wout is the energy from the discharge pulse and Win is the energy fromthe corresponding charging pulse.

The resistance parameters for the charging phase is analogous to the one above.

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3 MethodThe workflow for this project was defined in an early stage together with thethesis supervisor and a schematic overview of this workflow is depicted in fig-ure 10. The goal with this project was to develop a toolbox intended for datastorage and management of battery measurement data from the previous de-scribed field test equipment. Regarding managing the collected data, focus waslaid on determining and visualizing the aging of the battery cells. In order tostore all the field test data in one place a database was chosen to be used anda mathetmatical software was needed to be able to determine and visualize theaging of the cells. Already available software on Scania were Microsoft Accessand Matlab hence that software were a natural choice when implementing thedatabase and the mathematical calculations respectively. In addition a dataconversion program (called Readlog.exe), programmed in c, was available inorder to convert the binary data files into text files.

Figure 10: Schematic overview of the workflow created in the beginning of thethesis showing the di"erent parts of the project.

3.1 Database developmentBefore creating the database interviews were made with key people in order tofind out how to set up the database and what functions that had to be imple-mented. The database had to be able to import and export data. A displayingwindow where the data within the database could be displayed and filtered wasimplemented. In addition the import function had to be able to convert binarydata files from the test equipment into text files using the data conversion pro-gram. The data files contains thirteen parameters, which are displayed in Table2. A function that converts the absolute time that is available as a parameterinto dates in the format yyyy-mm-dd was developed for the purpose of beingused in the filtering function in the display window. Furthermore since the filetype was described in the extension of the file names, a function was writtenwhere each file extension was parsed and inserted in a separate column in thedatabase. In this way the data can be displayed both by date and file type. Inthe export function the parameters of interest can be chosen and as an extraoption the absolute time can be added as a separate parameter in the exporteddata file.

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ParametersCurrentVoltageTemperature1 (battery surface)Temperature2 (within case)Driveline torqueNelMachineCtrl strategyVehiclespeedChargeenergyDischargeenergyAbsolute timeControl modeSocBattery capacity

Table 2: Data parameters stored within the test equipment available from withinthe database.

Import function

The binary data files generated in the test equipment were originally convertedin the dos-prompt using the data conversion program, converting one file attime. By using the already existing built in import function interface in win-dows when programming the import function in the database files within adirectory could be filtered on type and multiple selections could be done whenimporting to the database. By using a Shell function when programming, dataconversion was made to be executed from within the database instead of as pre-viously executed from the dos-prompt. A Shell function is a tool which allowsusing DOS-commands when programming in Visual Basic. An extra field wasadded to the form in the database GUI so that the user can specify from whichdirectory the data conversion program should convert the binary data files intotext files. A problem that occurred when implementing the data conversionprogram this way that was the interpretation of file extensions within the Win-dows environment. The file extensions used were of type *.D00.txt, *.D02.txtand *.D03.txt, which made Windows interpreting the extension as ’*.D0*.txt’rather than ’.txt’ which was the wanted interpretation. In order to solve thisproblem the data conversion program was modified to generate extensions like*-D0*.txt with the ’.’ in front of the letter replaced with ’-’ instead.

Display window and filtering function

All the files that had been added to the database are made visible to the uservia a scroll list in the display window. The files are possible to filter by date andfile type. A function was written in order to make it possible to view the filedates (in format yyyy-mm-dd) in the display window based on the absolute timeparameter present within each file. By using the built in calendar in windows,the files can be filtered on di"erent time criteria such as ’before’, ’after’, ’on’or ’between’ (between specific dates chosen by the user). An extra column was

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added to the database table describing the file type extension. Each time a newfile is imported to the database the file name is parsed and the extension savedinto the file extension column in the database table, enabling filtering on filetype as well.

Export function

The export function simply exports user selected parameters filtrated by dateselections and file type chosen in the display window. As an additional optionthe absolute time can be added as a parameter when exporting since it is de-sirable when analyzing certain parameters. A problem that was not detectedimmediately occurred when exporting data. The database truncated the dataresulting in loss in accuracy. This problem was addressed by simply changingthe data type from ’double’ to ’text’. The text data type is usually used whenstoring characters.

3.2 Matlab script developmentThe field test data files that are of interest and stored in the database originatesfrom normal cycling, pulse tests and capacity test, and those files have fileextensions D00, D02 and D03 respectively. When a field test truck starts thereis often a warm up sequence where the battery is heated. During normal cycling,pulse- and capacity tests are made periodically. Parameters of interest regardingpulse- and capacity tests are SOC, voltage and current. From capacity test datacell voltage is plotted against charge. From pulse test data internal resistance forboth charge and discharge pulses are calculated as well as the energy e!ciency.The main problem when putting all the field test data together in the databaseis that pulse- and capacity tests may not be completed due to vehicle shutdown. The capacity test does not get a"ected as much as the pulse test since aninterruption in the capacity test simply yields a slight increase in SOC but stillcontinues from where it was ended when the vehicle starts again. (See figure11). In the pulse test, current pulses are made on three di"erent SOC levels (30,50 and 70%) where resistance parameters are calculated at four points in timefor each charge/discharge pulse. Since interruptions can occur anywhere in apulse the battery is charged to the next SOC level when the system is restarted(figure 12), only complete sequences where all four points in time for a specificSOC level is taken into consideration in this work.

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0 10 20 30 40 50 60 70 800

10

20

30

40

50

60

70

80

90

100

Time (min)

Soc

(%)

Figure 11: Capacity test showing discharging from SOC 100% down to 0%. Thetest does not get a"ected by interruptions since it restarts at the same pointin time where it stopped. (Seen at t=53 and 60 respectively). The di"erencesin SOC level at the interruption points shows some deficiencies in the SOCalgorithm implemented in the test equipment.

0 50 100 150 200 2500

10

20

30

40

50

60

70

Time (min)

SO

C (

%)

Figure 12: Pulse test showing the di"erent SOC levels where each level(30,50,70%) is representing the charge, discharge and step pulse shown in figure8.

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4 Results

4.1 DatabaseIn figure 13 the result of the finished database can be seen. The database hasan import function from where the user can browse and select multiple files. Anextra search path has also been added so that the program which is convertingthe binary data files into text files knows where to find the files.

In addition a ’display window’ where the user can see which files that havebeen loaded into the database is available. The user can filter the data bychoosing and specifying criteria’s based on date and file type. Date can bechosen as ’On’, ’After’, ’Before’ or ’Between’. When choosing the last criteriaan extra date field is displayed. File types available are D00, D02 and D03known as ’normal cycling’, ’pulse test’ and ’capacity test’ respectively. Dateand file types are filtered in ascending order.

Furthermore the user can export the data of interest based on the criteria’schosen in the display window. The user can also choose whether to include the’absolute time’ parameter or not.

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Figure 13: Result of the database displaying the import-, export function andfiltering functions. In the menu to the left the queries can be seen and belowthe modules where the Visual Basic code is written.

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4.2 Matlab scripts4.2.1 Capacity test

In figure 14 the results of the capacity tests from the ’blue’ truck during theperiod (2010-10-21 - 2011-07-01) can be seen. The voltage curve where thecapacity is 6 Ah is wrong due to test equipment problems. It can be seenstudying the curves that there is a quite big jump in the beginning from thefirst capacity tests from about 3,5 Ah down to 3,2. After that the capacity isstabilized and the lowest capacity measured so far during this time period is 2,8Ah.

0 1 2 3 4 5 6 71.8

2

2.2

2.4

2.6

2.8

3

Capacity (Ah)

Volta

ge (V

)

Figure 14: Capacity test from ’blue’ truck during time period 2010-10-21 -2011-07-01. The voltage curve where the capacity is 6 Ah is wrong due to testequipment problems.

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The results from the capacity tests made on the ’white’ truck during the timeperiod (2010-10-27 - 2011-02-27) (figure 15) also shows a couple of measuringerrors due to test equipment problems. Just as in the case with the ’blue’ truckthe capacity starts at around 3,5 Ah and stays pretty much around 3 Ah for thefollowing tests.

0 1 2 3 4 5 6 71.8

2

2.2

2.4

2.6

2.8

3

Capacity (Ah)

Vo

ltag

e (V

)

Figure 15: Capacity test from ’white’ truck during time period 2010-10-27 -2011-02-27. Capacity measurements in lab on the cells shows smaller capacityloss compared to the on board capacity measurements. This indicates that theon board capacity test sequence needs to be checked. The voltage curves wherethe capacity is around 6 Ah are wrong due to test equipment problems.

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4.2.2 Pulse test

The result from the pulse tests regarding the ’blue’ truck from the time period2010-10-21 - 2011-07-01 is displayed in figure 16. It shows the resistance mea-sured at four points in time for each charge- and discharge current pulse at 50%SOC. The resistance parameters are in the span of 1,8 to just above 3 milli ohm.The extreme values in the beginning of the graph is likely due to measurementerrors. The resistance parameters for the ’blue’ truck does not really revealany aging. If aging had started to occur, the values of the resistances in thelater part of the graph would have been significant higher than the values inthe beginning The e!ciency is as expected for a hybrid battery between 95- to97%.

0

1

2

3

4

5 50 % SOC

Relative pulse test time (h)

Res

ista

nce

(m!

)

DischargeCharge

0 5 10 15 20 25 300

20

40

60

80

100

Test number

Effi

cien

cy (%

)

33 38 44 50 56 61 67 72 78

Figure 16: Pulse test for the ’blue’ truck during time period 2010-10-21 - 2011-07-01 showing charge and discharge resistances and energy e!ciency.

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The pulse tests from the ’white’ truck (in figure 17) were generated from thetime period 2010-10-27 - 2011-02-27. The resistances are distributed within therange of 1,8 to about 3,5 milli ohm at 50% soc. The e!ciency is calculated tobe around 95 to 97%. As for the case of the ’blue’ truck no aging can be seenso far.

14 28 42 56 70 840

1

2

3

4

5 50 % SOC

Relative pulse test time (h)

Res

ista

nce

(m!

)

DischargeCharge

0 5 10 15 20 25 300

20

40

60

80

100

Test number

Effi

cien

cy (%

)

Figure 17: Pulse tests for the ’white’ truck during time period 2010-10-27 -2011-02-27 showing charge and discharge resistances and energy e!ciency. Thefirst eight points do not show any e!ciency due to test equipment problems.

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4.3 Drive cycle analysis4.3.1 Temperature

The temperature distribution of the battery cell on the ’blue’ truck can be seenin figure 18. The graph displays the temperature for the period 2010-10-21 -2011-07-01. The temperature peaks around 15 degrees. The reason for this isbecause the software in the test equipment is warming the battery to 15 degreeswhenever the battery temperature is less, for example during the colder monthsof the year. Higher temperatures are also shown in the graph which reflects thecases when the surrounding temperature is high, i.e during summer time.

−10 −5 0 5 10 15 20 25 30 35 400

0.5

1

1.5

2

2.5

3 x 106

Temperature (°C)

Den

sity

Figure 18: Temperature distribution of the ’blue’ truck for time period 2010-10-21 - 2011-07-01.

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As for the ’blue’ truck, the ’white’ truck also peaks around 15 degrees forthe same reasons as given above. The di"erence is though that the data for the’white’ truck in figure 19 has a narrower date range which reflects the monthsfrom october till january. Therefore the higher temperatures seen in the casefor the ’blue’ truck cannot be seen here.

−10 −5 0 5 10 15 20 25 300

2

4

6

8

10

12

14

16

18 x 105

Temperature (°C)

Den

sity

Figure 19: Temperature distribution of the ’white’ truck for time period 2010-10-21 - 2010-12-22.

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4.3.2 Vehiclespeed

It is also possible to use the database for displaying other than battery relatedparameters such as vehicle speed. In figure 20 the vehicle speed profile for the’blue’ truck can be seen. From the graph it is possible to see that the vehiclemostly has been driven within city areas and not so much on the freeway.

0 10 20 30 40 50 60 70 80 90 1000

0.5

1

1.5

2

2.5 x 105

Velocity (km/h)

Den

sity

Figure 20: Vehicle speed distribution for the ’blue’ truck for time period 2010-10-21 - 2011-07-01.

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In figure 21 the vehicle speed profile for the ’white’ truck can be seen. Asfor the case with the ’blue’ truck it has mostly been driven within city areasand not so much on freeways.

0 10 20 30 40 50 60 70 80 900

2

4

6

8

10

12x 104

Velocity (km/h)

Den

sity

Figure 21: Vehicle speed distribution for the ’white’ truck for time period 2010-10-21 - 2010-12-22.

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4.3.3 SOC

Another example of interesting parameter to analyze distribution over time foris SOC. In figure 22 the SOC distribution for the ’blue’ truck can be seen.From the figure it can be seen that the truck has a drive pattern where SOC isdistributed around 50%.

20 30 40 50 60 70 80 90 1000

0.5

1

1.5

2

2.5

3 x 105

SOC (%)

Den

sity

Figure 22: SOC distribution for the ’blue’ truck during time period 2010-10-21- 2011-07-01.

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The SOC distribution for the ’white’ truck peaks around 30% and can beseen in figure 23.

10 20 30 40 50 60 70 80 90 1000

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2x 105

SOC(%)

Den

sity

Figure 23: SOC distribution for the ’white’ truck during time period 2010-10-21- 2010-12-22. The reason why the SOC distribution is di"erent compared to the’blue’ truck is due to usage of di"erent hybrid strategies.

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5 Discussion and ConclusionsA fully functional database has been created based on the demands. The de-velopment of the database was more time consuming than expected due to theneed for studying of new program software and programming languages andtheir interactions.

The database can handle large quantities of data and can store up to 2 gi-gabyte of data. The database can import multiple files and order and sorts thedata within the files by date and file type. In addition the database can exportparameters from the data files based on the criteria’s date and file type. As aresult it is possible to create continuous data series over long time periods fromwhich it is possible to for example see trends in the aging of the battery cells.In addition to this it is also possible to perform drive cycle analysis from whichfor example battery temperature, vehicle speed and SOC distributions can beexamined. This is of course just a small selection of possible analysis and a lotmore can be examined from the fourteen available test parameters.

The choice of working with Microsoft Access as a database is not optimal sinceit has a limited storing capacity and spends unnecessary time searching throughdata. The Microsoft Access database is more suitable for working with systemslike libraries etcetera where the relational model is better used compared tothis case. A future improvement could be to transfer the current database ontoanother database platform which searches through data more e!ciently and hasthe ability of storing larger quantities of data. One option could be to transferthe Microsoft Access database to SQL-server which can store up to 10 GB dataand is more suitable for this type of data storage since it has a faster way ofhandling and searching through the stored data. This would also enable dataanalysis over larger time spans.

An improvement of the current database could be to make changes in the cur-rent test equipment software. Currently the sampling frequency is about 10 Hzor 100 milliseconds. Since the data rarely changes within that time span it couldeasily be reduced increasing the storing capacity in the current database.

When it comes to the aging of the battery cells the data that is available todaystretches over a too small interval in order to be able to see any changes such asdecrease in capacity or increase of resistance. The capacity tests for both trucks,’white’ and ’blue’, shows that there is initially a decrease in capacity only aftera few cycles but the capacity is then stabilized (around 3 Ah). However, thisbehavior seems to be related to the on board capacity test sequence since labmeasurements on same battery cells shows di"erent results. The end of life ofthe batteries is often defined as the point when the capacity has been reducedto about 80% of the initial capacity. If the initial capacity is around 3.5 Ah (asin the graphs shown in figure 14 and figure 15) this means that end of life forthe cells would be around 2.8 Ah.

Looking at the pulse tests in figure 16 and figure 17 the resistance for charg-ing and discharging pulses stays pretty much within the interval of 1,8 to 3,5milliohm for the ’blue’ truck and 1,8 to just above 3 milliohm for the ’white’

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truck. The e!ciency for both trucks stays the same all over the time periodwithin the interval of 95 to 97%, which is expected. It is expected to see theresistance increasing two to three times the present values when the battery isaged. Improvements concerning the scripts written for capacity and pulse testscan of course be made. The capacity test itself does not require much code butthe pulse test could be made more e!cient decreasing the executing time forcalculating the resistance parameters.

A lot more can be done using the database besides determining capacity andinner resistance. In addition to the pulse tests and capacity tests there arealso field test data from normal cycling. Since the data files and in turn thedatabase contains fourteen parameters a lot of information can be extracted anddetermined which could be useful when analyzing and understanding the agingof the cells. In the drive cycle analysis parameters such as temperature, vehi-cle speed and SOC have been extracted and analyzed using for example Matlab.

The temperature distributions in figure 18 and figure 19 for the ’blue’ and’white’ truck respectively displays the temperature on the battery. Within thedatabase there are two temperature parameters which can be chosen. The sec-ond one is the ambient temperature within the outer case and the first one isthe battery temperature. Two sensors have been placed onto the battery caseand since the material holding the battery in place conducts heat very well itis a good approximation that the temperature on the surface of the battery isabout the same within the battery. As displayed in the figures the temperaturepeaks around 15 degrees. This is due to the additional heat that is given to thebattery every time the temperature is below 15 degrees. Since it is desirableto have a temperature around 25 degrees in order for optimal performance andminimal aging it could be of interest knowing the temperature distribution ofthe battery over time in order to be able to use e.g cooling when temperatureincreases to much during certain time periods (i.e during summer). The vehiclespeed distributions shown in figure 20 and figure 21 gives information about thedrive patterns. This information could help seeing what kind of drive patternsthat are optimal in terms of aging of the cells, when correlated to the state ofcharge distributions shown in figure 22 and figure 23. As can be seen the distri-bution peaks around 50% for the ’blue’ truck whereas it peaks around 30% forthe ’white’ truck.

AcknowledgementsI would like to thank my supervisor Pontus Svens at Scania for all the supportand knowledge that he has provided me with during the project. I would alsolike to thank all the people at division NB at Scania that have helped me whenneeded. In addition I would also like to send my gratitude to Erik Zeitler at Up-psala university for guidance and strategy behind the database. Furthermore Iwould also like to thank my reviewer Torbjörn Gustafsson at Uppsala universityfor giving me insightful thoughts about my thesis.

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References[1] R. Elmasri and S. B. Navathe, Fundamentals of Database Systems. Pearson,

Addison-Wesley, 4th ed., 2004.

[2] http://www.wiley.com/college/busin/icmis/oakman/outline/chap08/slides/network.htm.

[3] http://www.wiley.com/college/busin/icmis/oakman/outline/chap08/slides/hier.htm.

[4] P. Svens, J. Lindstrom, O. Gelin, M. Behm, and G. Lindbergh, “Novel fieldtest equipment for lithium-ion batteries in hybrid electrical vehicle applica-tions,” Energies, vol. 4, no. 5, pp. 741–757, 2011.

[5] C. Nordling and J. Osterman, “Physics handbook for science and engineer-ing.”

[6] Goodenough, J. B, H. D. Abruna, and M. V. Buchanan, “Basic researchneeds for electrical energy storage,” 2007. Workshop on Basic Research Needsfor Electrical Energy Storage, O!ce of Basic Energy Sciences, Departmentof Energy.

[7] J. Vetter, P. Novak, M. Wagner, C. Veit, K. Moller, J. Besenhard, M. Win-ter, M. Wohlfahrt-Mehrens, C. Vogler, and A. Hammouche, “Ageing mecha-nisms in lithium-ion batteries,” Journal of Power Sources, vol. 147, no. 1-2,pp. 269–281, 2005.

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